How to Integrate AI in SDLC: Wiseboard’s Approach

In this article

By Alex Sharko
AI Practice Lead • Head of AI at Clearly

The biggest fear companies have about AI in software development is complexity.
And I get it. There are no industry standards yet, no plug-and-play frameworks. At best, you hear a few success stories from individual teams. That’s what a paradigm shift looks like, the same thing we saw with DevOps, now common. Before long, AI in SDLC will become the standard, too.
If your leads are already asking “How do you use AI in development?” you know the shift has begun. The good news is, Wiseboard is already helping IT companies adapt. In this article, I’ll show you how we approach AI in SDLC through the program we’ve built and run with Wiseboard’s clients.
P.S. Integrating AI in SDLC is one of three AI rollout paths I described in another Wiseboard article.

But first, why is now the right moment to experiment with AI in your SDLC?

Why now is the best time to start with AI in SDLC

AI is reshaping how software gets built, and there are no industry standards yet. That’s why teams that start with AI early have an advantage: they can experiment, learn, and set the rules before everyone else catches up.
Early AI adopters already see results. SoftServe reports a 45% boost in team productivity and a 30% reduction in project timelines.
Here’s what structured AI adoption can look like across the software development cycle. There are clear roles for AI at every stage of delivery.

AI helps every step of SDLC, from requirements gathering to deployment. Source: Dr. Priyanka Nair

Say, your client is asking: “How do you use AI in development?”
Chances are, some of your developers already experiment with tools like GitHub Copilot, Cursor, Claude Code, or Windsurf. But that’s individual use, like everyone using ChatGPT in their own way. Without a company-wide AI SDLC policy, backed by metrics, you don’t yet have an established practice.
That’s when you need a documented AI in SDLC policy that highlights what you do with AI, what you don’t, and how you keep the process safe and repeatable.
In practice, it usually starts as a short one- or two-page document that defines:

which tools are approved (assistants, agents, or LLM bots across the SDLC)

which tasks AI should or shouldn’t handle

how security and code review are enforced

how the policy evolves as tools improve

The policy might include points like “Never build sensitive modules (like finance) purely with Copilot” or “Always use AI for high-efficiency tasks such as API integrations and test coverage.”
Note that AI in SDLC policy is a living document. For example, the first version may only cover developers. Later, it can extend to QA (AI-generated test cases), presales (AI-driven proposals), or DevOps (AI monitoring scripts). It grows with your team and processes.

Step 1. Audit delivery

We start with how you work today. Which tools are in use? How do you handle code reviews, tickets, QA, and DevOps? We look at security gaps and check what small DevOps changes are needed, like adding scanners or review rules for AI-generated code.

Smaller companies usually begin with coding assistants like Copilot or Cursor, and later extend to coding agents (like Codex) that integrate into an IDE.

Bigger companies often combine coding assistants with LLM bots across the entire SDLC because parallel adoption is more feasible for them.

Case in point: we helped a 500-person IT company map their SDLC and found they could integrate LLM bots at every stage, from presale to client support.

Use cases for LLM bots across the SDLC

✔ The outcome

First version of your AI SDLC policy that sets boundaries, rules, and approved tools.

✔ The outcome

Evidence of AI’s impact on real projects, plus feedback from early users.

✔ The outcome

A small group of AI champions who promote AI adoption across other teams.

✔ The outcome

AI in SDLC becomes part of everyday work, supported by proof and internal culture.

✔ The outcome

A company-wide AI SDLC policy you can show to clients as a sales advantage.

Metrics to measure before and during your AI in SDLC initiative

A lack of clear metrics is the biggest AI challenge for 60% of engineering leaders, according to LeadDev’s 2025 AI Impact Report. But that doesn’t mean we need brand-new KPIs. AI doesn’t change what “good” software looks like. Quality, maintainability, and speed still matter most. The real question is whether AI helps you improve on those fundamentals.
Many of the metrics tech teams measure aren’t new (like pull request throughput). What’s new are AI-usage signals layered on top: time savings, AI spend, AI tool usage.

How 9 top companies measure AI impact. Source: The Pragmatic Engineer

We recommend you start tracking the basic set of metrics, and then expand to advanced ones.

Basic set of metrics to measure

Developer contribution → commits with and without AI-generated code, PRs with and without AI contributions, frequency of AI tool usage (active users, tasks assisted, suggestions accepted).

Bug counts and defect types → are issues going up or down post-adoption?

Security vulnerabilities → what scanners find in the pipeline, and whether risk is shrinking or spiking.

Onboarding speed → AI tools help explain code, summarize documentation, and speed up how fast new developers get productive.

Advanced set of metrics

Because the minimal set is easy to track but doesn’t prove quality, we also recommend measuring outcomes that tie directly to effectiveness and business value.
DORA metrics (DevOps and delivery health):

Deployment frequency → how often code reaches production.

Lead time for changes → how fast a commit goes live.

Change failure rate → how many releases cause issues.

Mean time to recovery (MTTR) → how quickly the team restores service after failure.

Earned value per sprint (business outcomes):

Services companies → billable features delivered, or backlog items linked to client invoices.

Product companies → new capabilities for end users, performance gains that improve retention, or reduced churn.

Developer experience frameworks (DevEx / SPACE):

Services companies → billable features delivered, or backlog items linked to client invoices.

Product companies → new capabilities for end users, performance gains that improve retention, or reduced churn.

Change failure rate → how many releases cause issues.

These frameworks measure both developer’s output and well-being.
Case in point: My team ran a pilot for one of the UK’s top four banks. In it, two teams adopted GitLab Duo and GitHub Copilot. With security metrics and value tracking, onboarding time dropped from 58 days to 30. For an organization with 10,000 engineers, those 28 saved days translated into £7M of annual value.
Of course, not every company has 10,000 developers. For smaller teams, the impact looks different but still significant: some double their release speed, others slash onboarding time for new hires.